A tomographic approach to non-Markovian master equations

We propose a procedure based on symplectic tomography for reconstructing the unknown parameters of a convolutionless non-Markovian Gaussian noisy evolution. Whenever the time-dependent master equation coefficients are given as a function of some unknown time-independent parameters, we show that these parameters can be reconstructed by means of a finite number of tomograms. Two different approaches towards reconstruction, integral and differential, are presented and applied to a benchmark model made of a harmonic oscillator coupled to a bosonic bath. For this model the number of tomograms needed to retrieve the unknown parameters is explicitly computed.Comment: 15 pages, 2 figure

Reconstruction of Markovian Master Equation parameters through symplectic tomography

In open quantum systems, phenomenological master equations with unknown parameters are often introduced. Here we propose a time-independent procedure based on quantum tomography to reconstruct the potentially unknown parameters of a wide class of Markovian master equations. According to our scheme, the system under investigation is initially prepared in a Gaussian state. At an arbitrary time t, in order to retrieve the unknown coefficients one needs to measure only a finite number (ten at maximum) of points along three time-independent tomograms. Due to the limited amount of measurements required, we expect our proposal to be especially suitable for experimental implementations.Comment: 7 pages, 3 figure

Enabling Depth-driven Visual Attention on the iCub Humanoid Robot: Instructions for Use and New Perspectives

The importance of depth perception in the interactions that humans have within their nearby space is a well established fact. Consequently, it is also well known that the possibility of exploiting good stereo information would ease and, in many cases, enable, a large variety of attentional and interactive behaviors on humanoid robotic platforms. However, the difficulty of computing real-time and robust binocular disparity maps from moving stereo cameras often prevents from relying on this kind of cue to visually guide robots' attention and actions in real-world scenarios. The contribution of this paper is two-fold: first, we show that the Efficient Large-scale Stereo Matching algorithm (ELAS) by A. Geiger et al. 2010 for computation of the disparity map is well suited to be used on a humanoid robotic platform as the iCub robot; second, we show how, provided with a fast and reliable stereo system, implementing relatively challenging visual behaviors in natural settings can require much less effort. As a case of study we consider the common situation where the robot is asked to focus the attention on one object close in the scene, showing how a simple but effective disparity-based segmentation solves the problem in this case. Indeed this example paves the way to a variety of other similar applications

Industrial Landscapes Between Environmental Sustainability and Landscape Constraints: The Case Study of Euralluminia in the Sulcis Area of Sardinia (Italy)

In Italy, industrialization had a remarkable development in the 1950s and 1960s, and aimed with priority of ensuring economic growth and development. The location of the industrial complexes was determined by the dynamics of the production that required a territory equipped to supply specific infrastructures such as water connections, sewers, gas pipelines and the electricity grid, and above all areas where to build transport terminals capable of mitigating the costs of handling the product. This led Italy to locate industrial activities in many coastal sites, close to pre-existing urban contexts, resulting in a well-defined coastal industrial landscape especially in the areas of Southern Italy that were chosen as centers of development. Today, the determining factor for location choices is the cost of the workforce and this has made more and more frequent the processes of delocalization of the companies with worrying repercussions both for the direct and induced occupation and for the degradation of the landscape. This process, linked to the safety regulations, to the updating of the systems and to an increasingly more rigorous landscape legislation, makes critical the framework of the existing and not yet abandoned disused industrial realities. For these reasons, the main objective of this article is to evaluate the compatibility between existing industrial areas at risk of delocalization and new interpretations of the environment and the landscape to be reconstituted, in order to allow the realization of goods that maintain the levels of industrial production within a framework ofecological protection rules and recently adopted landscape constraints. In this regard, in this paper the authors use the Eurallumina industry in Sulcis in Sardinia (Italy) as a case study, in order to analyze the problem that concerns the uses in the territories with an industrial vocation and the landscape components, that deserve particular attention to safeguard not only for the economic and social context but also for the quality of the coastal environment. The case study is particularly significant because the Euralluminia industry for some years was at risk of delocalization because it needs of a conversion of some parts of the plants, blocked due to the landscape regulation imposed by the Superintendence of Cultural Heritage ofSouthern Sardinia for the expected changes in the coastal environment. Therefore, keeping in mind the theories of localization and the pushes for the delocalization of the industrial contexts, the study discusses the importance of the interconnection between economic and landscape factors paying particular attention to the coastal areas

On the Herdability of Linear Time-Invariant Systems with Special Topological Structures

In this paper, we investigate the herdability property, namely the capability of a system to be driven towards the (interior of the) positive orthant, for linear time-invariant state-space models. Herdability of certain matrix pairs (A,B), where A is the adjacency matrix of a multi-agent network, and B is a selection matrix that singles out a subset of the agents (the "network leaders"), is explored. The cases when the graph associated with A, G(A), is directed and clustering balanced (in particular, structurally balanced), or it has a tree topology and there is a single leader, are investigated.Comment: Provisionally accepted in Automatica, currently under review. arXiv admin note: substantial text overlap with arXiv:2108.0157

Modeling the Cooperative Process of Learning a Task

In this paper, we propose a mathematical model for a Transactive Memory System (TMS) involved in the cooperative process of learning a task. The model is based on an intertwined dynamics involving both the individuals level of expertise and the interaction network among the cooperators. The model shows that if all the agents are non-stubborn, then all of them are able to acquire the competence of the most expert members of the group, asymptotically reaching their level of proficiency. Conversely, when dealing with all stubborn agents, the capability to pass on the task depends on the connectedness properties of the interaction graph.Comment: Accepted for presentation at the European Control Conference (ECC 2022

Multi-dimensional extensions of the Hegselmann-Krause model

In this paper, we consider two multi-dimensional Hagselmann-Krause (HK) models for opinion dynamics. The two models describe how individuals adjust their opinions on multiple topics, based on the influence of their peers. The models differ in the criterion according to which individuals decide whom they want to be influenced from. In the average-based model, individuals compare their average opinions on the various topics with those of the other individuals and interact only with those individuals whose average opinions lie within a confidence interval. For this model, we provide an alternative proof for the contractivity of the range of opinions and show that the agents' opinions reach consensus/clustering if and only if their average opinions do so. In the uniform affinity model agents compare their opinions on every single topic and influence each other only if, topic-wise, such opinions do not differ more than a given tolerance. We identify conditions under which the uniform affinity model enjoys the order-preservation property topic-wise and we prove that the global range of opinions (and hence the range of opinions on every single topic) are nonincreasing.Comment: Submitted to the 61st Conference on Decision and Control (CDC 2022), Cancun, Mexic